





import argparse
import logging
import os
import re

import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    DataCollatorForLanguageModeling,
    PushToHubCallback,
    TFAutoModelForMaskedLM,
    create_optimizer,
)


__lowercase	: Tuple       =logging.getLogger(__name__)

__lowercase	: Optional[int]       =tf.data.AUTOTUNE




def      a__			(       ):






   '''simple docstring'''






   UpperCAmelCase_           =argparse.ArgumentParser(description="Train a masked language model on TPU."	)
   parser.add_argument(
       "--pretrained_model_config"	,     type=lowercase__	,     default="roberta-base"	,     help="The model config to use. Note that we don't copy the model's weights, only the config!"	,     )
   parser.add_argument(
       "--tokenizer"	,     type=lowercase__	,     default="unigram-tokenizer-wikitext"	,     help="The name of the tokenizer to load. We use the pretrained tokenizer to initialize the model's vocab size."	,     )

   parser.add_argument(
       "--per_replica_batch_size"	,     type=lowercase__	,     default=8	,     help="Batch size per TPU core."	,     )

   parser.add_argument(
       "--no_tpu"	,     action="store_true"	,     help="If set, run on CPU and don't try to initialize a TPU. Useful for debugging on non-TPU instances."	,     )

   parser.add_argument(
       "--tpu_name"	,     type=lowercase__	,     help="Name of TPU resource to initialize. Should be blank on Colab, and 'local' on TPU VMs."	,     default="local"	,     )

   parser.add_argument(
       "--tpu_zone"	,     type=lowercase__	,     help="Google cloud zone that TPU resource is located in. Only used for non-Colab TPU nodes."	,     )

   parser.add_argument(
       "--gcp_project"	,     type=lowercase__	,     help="Google cloud project name. Only used for non-Colab TPU nodes."	)

   parser.add_argument(
       "--bfloat16"	,     action="store_true"	,     help="Use mixed-precision bfloat16 for training. This is the recommended lower-precision format for TPU."	,     )

   parser.add_argument(
       "--train_dataset"	,     type=lowercase__	,     help="Path to training dataset to load. If the path begins with `gs://`"
       " then the dataset will be loaded from a Google Cloud Storage bucket."	,     )

   parser.add_argument(
       "--shuffle_buffer_size"	,     type=lowercase__	,     default=2**1_8	,     help="Size of the shuffle buffer (in samples)"	,     )

   parser.add_argument(
       "--eval_dataset"	,     type=lowercase__	,     help="Path to evaluation dataset to load. If the path begins with `gs://`"
       " then the dataset will be loaded from a Google Cloud Storage bucket."	,     )

   parser.add_argument(
       "--num_epochs"	,     type=lowercase__	,     default=1	,     help="Number of epochs to train for."	,     )

   parser.add_argument(
       "--learning_rate"	,     type=lowercase__	,     default=1E-4	,     help="Learning rate to use for training."	,     )

   parser.add_argument(
       "--weight_decay_rate"	,     type=lowercase__	,     default=1E-3	,     help="Weight decay rate to use for training."	,     )

   parser.add_argument(
       "--max_length"	,     type=lowercase__	,     default=5_1_2	,     help="Maximum length of tokenized sequences. Should match the setting used in prepare_tfrecord_shards.py"	,     )

   parser.add_argument(
       "--mlm_probability"	,     type=lowercase__	,     default=0.15	,     help="Fraction of tokens to mask during training."	,     )

   parser.add_argument("--output_dir"	,     type=lowercase__	,     required=lowercase__	,     help="Path to save model checkpoints to."	)
   parser.add_argument("--hub_model_id"	,     type=lowercase__	,     help="Model ID to upload to on the Hugging Face Hub."	)

   UpperCAmelCase_           =parser.parse_args()
   return args




def      a__			(       lowercase__	):






   '''simple docstring'''






   try:
      if args.tpu_name:
         UpperCAmelCase_           =tf.distribute.cluster_resolver.TPUClusterResolver(
             args.tpu_name	,     zone=args.tpu_zone	,     project=args.gcp_project	)
      else:
         UpperCAmelCase_           =tf.distribute.cluster_resolver.TPUClusterResolver()
   except ValueError:
      raise RuntimeError(
          "Couldn't connect to TPU! Most likely you need to specify --tpu_name, --tpu_zone, or "
          "--gcp_project. When running on a TPU VM, use --tpu_name local."	)

   tf.config.experimental_connect_to_cluster(lowercase__	)
   tf.tpu.experimental.initialize_tpu_system(lowercase__	)

   return tpu




def      a__			(       lowercase__	):






   '''simple docstring'''






   UpperCAmelCase_           =0
   for file in file_list:
      UpperCAmelCase_           =file.split("/"	)[-1]
      UpperCAmelCase_           =re.search(R"-\d+-(\d+)\.tfrecord"	,     lowercase__	).group(1	)
      UpperCAmelCase_           =int(lowercase__	)
      num_samples += sample_count

   return num_samples




def      a__			(       lowercase__	,     lowercase__	,     lowercase__	,     lowercase__	,     lowercase__	,     lowercase__=None	):






   '''simple docstring'''






   UpperCAmelCase_           =count_samples(lowercase__	)
   UpperCAmelCase_           =tf.data.Dataset.from_tensor_slices(lowercase__	)
   if shuffle:
      UpperCAmelCase_           =dataset.shuffle(len(lowercase__	)	)
   UpperCAmelCase_           =tf.data.TFRecordDataset(lowercase__	,     num_parallel_reads=lowercase__	)
   # TF can't infer the total sample count because it doesn't read all the records yet, so we assert it here
   UpperCAmelCase_           =dataset.apply(tf.data.experimental.assert_cardinality(lowercase__	)	)
   UpperCAmelCase_           =dataset.map(lowercase__	,     num_parallel_calls=lowercase__	)
   if shuffle:
      assert shuffle_buffer_size is not None
      UpperCAmelCase_           =dataset.shuffle(args.shuffle_buffer_size	)
   UpperCAmelCase_           =dataset.batch(lowercase__	,     drop_remainder=lowercase__	)
   UpperCAmelCase_           =dataset.map(lowercase__	,     num_parallel_calls=lowercase__	)
   UpperCAmelCase_           =dataset.prefetch(lowercase__	)
   return dataset




def      a__			(       lowercase__	):






   '''simple docstring'''






   if not args.no_tpu:
      UpperCAmelCase_           =initialize_tpu(lowercase__	)
      UpperCAmelCase_           =tf.distribute.TPUStrategy(lowercase__	)
   else:
      UpperCAmelCase_           =tf.distribute.OneDeviceStrategy(device="/gpu:0"	)

   if args.bfloataa:
      tf.keras.mixed_precision.set_global_policy("mixed_bfloat16"	)

   UpperCAmelCase_           =AutoTokenizer.from_pretrained(args.tokenizer	)
   UpperCAmelCase_           =AutoConfig.from_pretrained(args.pretrained_model_config	)
   UpperCAmelCase_           =tokenizer.vocab_size

   UpperCAmelCase_           =tf.io.gfile.glob(os.path.join(args.train_dataset	,     "*.tfrecord"	)	)
   if not training_records:
      raise ValueError(F'No .tfrecord files found in {args.train_dataset}.'	)
   UpperCAmelCase_           =tf.io.gfile.glob(os.path.join(args.eval_dataset	,     "*.tfrecord"	)	)
   if not eval_records:
      raise ValueError(F'No .tfrecord files found in {args.eval_dataset}.'	)

   UpperCAmelCase_           =count_samples(lowercase__	)

   UpperCAmelCase_           =num_train_samples // (args.per_replica_batch_size * strategy.num_replicas_in_sync)
   UpperCAmelCase_           =steps_per_epoch * args.num_epochs

   with strategy.scope():
      UpperCAmelCase_           =TFAutoModelForMaskedLM.from_config(lowercase__	)
      model(model.dummy_inputs	)  # Pass some dummy inputs through the model to ensure all the weights are built
      UpperCAmelCase_    ,							UpperCAmelCase_           =create_optimizer(
          num_train_steps=lowercase__	,     num_warmup_steps=total_train_steps // 2_0	,     init_lr=args.learning_rate	,     weight_decay_rate=args.weight_decay_rate	,     )

      # Transformers models compute the right loss for their task by default when labels are passed, and will
      # use this for training unless you specify your own loss function in compile().
      model.compile(optimizer=lowercase__	,     metrics=["accuracy"]	)

   def decode_fn(lowercase__	):
      UpperCAmelCase_           ={
          "input_ids": tf.io.FixedLenFeature(dtype=tf.intaa	,     shape=(args.max_length,)	),
          "attention_mask": tf.io.FixedLenFeature(dtype=tf.intaa	,     shape=(args.max_length,)	),
      }
      return tf.io.parse_single_example(lowercase__	,     lowercase__	)

   # Many of the data collators in Transformers are TF-compilable when return_tensors == "tf", so we can
   # use their methods in our data pipeline.
   UpperCAmelCase_           =DataCollatorForLanguageModeling(
       tokenizer=lowercase__	,     mlm_probability=args.mlm_probability	,     mlm=lowercase__	,     return_tensors="tf"	)

   def mask_with_collator(lowercase__	):
      # TF really needs an isin() function
      UpperCAmelCase_           =(
          ~tf.cast(batch["attention_mask"]	,     tf.bool	)
          | (batch["input_ids"] == tokenizer.cls_token_id)
          | (batch["input_ids"] == tokenizer.sep_token_id)
      )
      UpperCAmelCase_    ,							UpperCAmelCase_           =data_collator.tf_mask_tokens(
          batch["input_ids"]	,     vocab_size=len(lowercase__	)	,     mask_token_id=tokenizer.mask_token_id	,     special_tokens_mask=lowercase__	,     )
      return batch

   UpperCAmelCase_           =args.per_replica_batch_size * strategy.num_replicas_in_sync

   UpperCAmelCase_           =prepare_dataset(
       lowercase__	,     decode_fn=lowercase__	,     mask_fn=lowercase__	,     batch_size=lowercase__	,     shuffle=lowercase__	,     shuffle_buffer_size=args.shuffle_buffer_size	,     )

   UpperCAmelCase_           =prepare_dataset(
       lowercase__	,     decode_fn=lowercase__	,     mask_fn=lowercase__	,     batch_size=lowercase__	,     shuffle=lowercase__	,     )

   UpperCAmelCase_           =[]
   if args.hub_model_id:
      callbacks.append(
          PushToHubCallback(output_dir=args.output_dir	,     hub_model_id=args.hub_model_id	,     tokenizer=lowercase__	)	)

   model.fit(
       lowercase__	,     validation_data=lowercase__	,     epochs=args.num_epochs	,     callbacks=lowercase__	,     )

   model.save_pretrained(args.output_dir	)


if __name__ == "__main__":
     __lowercase	: Union[str, Any]       =parse_args()
     main(args)












'''simple docstring'''







import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()




class   __SCREAMING_SNAKE_CASE       (__A							,       __A							,       unittest.TestCase       ):


  """simple docstring"""


  _a  : str      			=     CycleDiffusionPipeline
  _a  : int      			=     TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
      '''negative_prompt''',
      '''height''',
      '''width''',
      '''negative_prompt_embeds''',
  }
  _a  : str      			=     PipelineTesterMixin.required_optional_params - {'''latents'''}
  _a  : str      			=     TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'''source_prompt'''}       )
  _a  : Optional[Any]      			=     IMAGE_TO_IMAGE_IMAGE_PARAMS
  _a  : Optional[int]      			=     IMAGE_TO_IMAGE_IMAGE_PARAMS







  def    _a       (			self	):






         """simple docstring"""


         torch.manual_seed(0	)
         a_						     =			UNetaDConditionModel(
             block_out_channels=(32, 64)   ,	layers_per_block=2   ,	sample_size=32   ,	in_channels=4   ,	out_channels=4   ,	down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')   ,	up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')   ,	cross_attention_dim=32   ,	)
         a_						     =			DDIMScheduler(
             beta_start=0.00_085   ,	beta_end=0.012   ,	beta_schedule='scaled_linear'   ,	num_train_timesteps=1_000   ,	clip_sample=UpperCamelCase__   ,	set_alpha_to_one=UpperCamelCase__   ,	)
         torch.manual_seed(0	)
         a_						     =			AutoencoderKL(
             block_out_channels=[32, 64]   ,	in_channels=3   ,	out_channels=3   ,	down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']   ,	up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']   ,	latent_channels=4   ,	)
         torch.manual_seed(0	)
         a_						     =			CLIPTextConfig(
             bos_token_id=0   ,	eos_token_id=2   ,	hidden_size=32   ,	intermediate_size=37   ,	layer_norm_eps=1e-05   ,	num_attention_heads=4   ,	num_hidden_layers=5   ,	pad_token_id=1   ,	vocab_size=1_000   ,	)
         a_						     =			CLIPTextModel(UpperCamelCase__	)
         a_						     =			CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'	)

         a_						     =			{
             'unet': unet,
             'scheduler': scheduler,
             'vae': vae,
             'text_encoder': text_encoder,
             'tokenizer': tokenizer,
             'safety_checker': None,
             'feature_extractor': None,
         }
         return components







  def    _a       (			self   ,	UpperCamelCase__   ,	UpperCamelCase__=0	):






         """simple docstring"""


         a_						     =			floats_tensor((1, 3, 32, 32)   ,	rng=random.Random(UpperCamelCase__	)	).to(UpperCamelCase__	)
         a_						     =			image / 2 + 0.5
         if str(UpperCamelCase__	).startswith('mps'	):
                a_						     =			torch.manual_seed(UpperCamelCase__	)
         else:
                a_						     =			torch.Generator(device=UpperCamelCase__	).manual_seed(UpperCamelCase__	)
         a_						     =			{
             'prompt': 'An astronaut riding an elephant',
             'source_prompt': 'An astronaut riding a horse',
             'image': image,
             'generator': generator,
             'num_inference_steps': 2,
             'eta': 0.1,
             'strength': 0.8,
             'guidance_scale': 3,
             'source_guidance_scale': 1,
             'output_type': 'numpy',
         }
         return inputs







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			'cpu'  # ensure determinism for the device-dependent torch.Generator

         a_						     =			self.get_dummy_components()
         a_						     =			CycleDiffusionPipeline(**UpperCamelCase__	)
         a_						     =			pipe.to(UpperCamelCase__	)
         pipe.set_progress_bar_config(disable=UpperCamelCase__	)

         a_						     =			self.get_dummy_inputs(UpperCamelCase__	)
         a_						     =			pipe(**UpperCamelCase__	)
         a_						     =			output.images

         a_						     =			images[0, -3:, -3:, -1]

         assert images.shape == (1, 32, 32, 3)
         a_						     =			np.array([0.4_459, 0.4_943, 0.4_544, 0.6_643, 0.5_474, 0.4_327, 0.5_701, 0.5_959, 0.5_179]	)

         assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-2







  @unittest.skipIf(torch_device != 'cuda'   ,	'This test requires a GPU'	)
  def    _a       (			self	):






         """simple docstring"""


         a_						     =			self.get_dummy_components()
         for name, module in components.items():
                if hasattr(UpperCamelCase__   ,	'half'	):
                       a_						     =			module.half()
         a_						     =			CycleDiffusionPipeline(**UpperCamelCase__	)
         a_						     =			pipe.to(UpperCamelCase__	)
         pipe.set_progress_bar_config(disable=UpperCamelCase__	)

         a_						     =			self.get_dummy_inputs(UpperCamelCase__	)
         a_						     =			pipe(**UpperCamelCase__	)
         a_						     =			output.images

         a_						     =			images[0, -3:, -3:, -1]

         assert images.shape == (1, 32, 32, 3)
         a_						     =			np.array([0.3_506, 0.4_543, 0.446, 0.4_575, 0.5_195, 0.4_155, 0.5_273, 0.518, 0.4_116]	)

         assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-2







  @skip_mps
  def    _a       (			self	):






         """simple docstring"""


         return super().test_save_load_local()







  @unittest.skip('non-deterministic pipeline'	)
  def    _a       (			self	):






         """simple docstring"""


         return super().test_inference_batch_single_identical()







  @skip_mps
  def    _a       (			self	):






         """simple docstring"""


         return super().test_dict_tuple_outputs_equivalent()







  @skip_mps
  def    _a       (			self	):






         """simple docstring"""


         return super().test_save_load_optional_components()







  @skip_mps
  def    _a       (			self	):






         """simple docstring"""


         return super().test_attention_slicing_forward_pass()





@slow
@require_torch_gpu
class   __SCREAMING_SNAKE_CASE       (unittest.TestCase       ):


  """simple docstring"""


  def    _a       (			self	):






         """simple docstring"""


         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			load_image(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/cycle-diffusion/black_colored_car.png'	)
         a_						     =			load_numpy(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy'	)
         a_						     =			init_image.resize((512, 512)	)

         a_						     =			'CompVis/stable-diffusion-v1-4'
         a_						     =			DDIMScheduler.from_pretrained(UpperCamelCase__   ,	subfolder='scheduler'	)
         a_						     =			CycleDiffusionPipeline.from_pretrained(
             UpperCamelCase__   ,	scheduler=UpperCamelCase__   ,	safety_checker=UpperCamelCase__   ,	torch_dtype=torch.floataa   ,	revision='fp16'	)

         pipe.to(UpperCamelCase__	)
         pipe.set_progress_bar_config(disable=UpperCamelCase__	)
         pipe.enable_attention_slicing()

         a_						     =			'A black colored car'
         a_						     =			'A blue colored car'

         a_						     =			torch.manual_seed(0	)
         a_						     =			pipe(
             prompt=UpperCamelCase__   ,	source_prompt=UpperCamelCase__   ,	image=UpperCamelCase__   ,	num_inference_steps=100   ,	eta=0.1   ,	strength=0.85   ,	guidance_scale=3   ,	source_guidance_scale=1   ,	generator=UpperCamelCase__   ,	output_type='np'   ,	)
         a_						     =			output.images

         # the values aren't exactly equal, but the images look the same visually
         assert np.abs(image - expected_image	).max() < 5e-1







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			load_image(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/cycle-diffusion/black_colored_car.png'	)
         a_						     =			load_numpy(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy'	)
         a_						     =			init_image.resize((512, 512)	)

         a_						     =			'CompVis/stable-diffusion-v1-4'
         a_						     =			DDIMScheduler.from_pretrained(UpperCamelCase__   ,	subfolder='scheduler'	)
         a_						     =			CycleDiffusionPipeline.from_pretrained(UpperCamelCase__   ,	scheduler=UpperCamelCase__   ,	safety_checker=UpperCamelCase__	)

         pipe.to(UpperCamelCase__	)
         pipe.set_progress_bar_config(disable=UpperCamelCase__	)
         pipe.enable_attention_slicing()

         a_						     =			'A black colored car'
         a_						     =			'A blue colored car'

         a_						     =			torch.manual_seed(0	)
         a_						     =			pipe(
             prompt=UpperCamelCase__   ,	source_prompt=UpperCamelCase__   ,	image=UpperCamelCase__   ,	num_inference_steps=100   ,	eta=0.1   ,	strength=0.85   ,	guidance_scale=3   ,	source_guidance_scale=1   ,	generator=UpperCamelCase__   ,	output_type='np'   ,	)
         a_						     =			output.images

         assert np.abs(image - expected_image	).max() < 2e-2




